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从基础研究浅析人工智能技术发展趋势
2020年电子技术应用第10期
李美桃
国家工业信息安全发展研究中心人工智能所,北京100040
摘要: 近六十多年来,人工智能在算法、算力和数据的共同驱动下,获得了飞速发展,但仍处于弱人工智能阶段。重点分析了人工智能算法和算力方面的基础研究现状和发展趋势,弱人工智能迈向强人工智能亟待基础研究上的革命性突破。算法层面,深度学习算法模型缺乏可释性和可泛化性,在基础理论上遇到瓶颈,亟待基础理论上的突破;算力层面,因集成电路工艺制程逼近微观物理极限导致摩尔定律失效和电子芯片算力增长趋缓,通用计算芯片架构受制于冯诺依曼瓶颈,以神经形态芯片为代表的人工智能芯片方兴未艾;数据层面,细分领域的高质量数据集匮乏制约人工智能技术应用发展,未来高质量数据集将不断构建。总之,人工智能底层技术将在未来相当长时间内缓慢前进,但产业化应用正在蓬勃发展。
中圖分類號: TP301
文獻標(biāo)識碼: A
DOI:10.16157/j.issn.0258-7998.200346
中文引用格式: 李美桃. 從基礎(chǔ)研究淺析人工智能技術(shù)發(fā)展趨勢[J].電子技術(shù)應(yīng)用,2020,46(10):29-33,38.
英文引用格式: Li Meitao. Analysis of the trend of artificial intelligence technology on basic research[J]. Application of Electronic Technique,2020,46(10):29-33,38.
Analysis of the trend of artificial intelligence technology on basic research
Li Meitao
National Industrial Information Security Development Research Center,Beijing 100040,China
Abstract: During the past sixty years, artificial intelligence(AI) has achieved rapid development jointly promoted by algorithms, computing power, and big data, but it is still in the stage of artificial narrow intelligence. The status and trends of basic research in AI algorithms and computing power are analyzed. The evolution of artificial narrow intelligence to artificial general intelligence will depend on breakthrough in AI basic theory research. On the aspect of AI algorithms, the deep learning algorithm model lacks interpretive reasoning and generalizability. AI encounters bottlenecks in basic theory and urgently needs a breakthrough. On the aspect of computing power, due to the CMOS physical limits the Moore′s law is approaching failure and the growth of computing power is slowing down, the general computing chip architecture is limited by Feng Neumann′s bottleneck and AI chips represented by neuromorphic chips are in the ascendant. On the aspect of data, the lack of high-quality data sets in specific area restricts AI technology application and more high-quality data sets will be continuously constructed in the short future. In short, the basic AI technology will slowly advance for a long time in the future, but the AI applications are booming from right now.
Key words : artificial intelligence;basic research;development trend;algorithm;computing power

0 引言

    人工智能(Artificial Intelligence,AI)是計算機技術(shù)發(fā)展到高級階段的復(fù)雜技術(shù)體系,綜合了計算機、數(shù)學(xué)、邏輯、信息論、控制論、認知科學(xué)和倫理學(xué)等多種學(xué)科。人工智能于1956年在達特茅斯學(xué)院的一次學(xué)術(shù)會議上被提出,可分為三個發(fā)展階段:弱人工智能(Artificial Narrow Intelligence,ANI)、強人工智能(Artificial General Intelligence,AGI)和超人工智能(Artificial Super Intelligence,ASI)。ANI是在限定條件下的人工智能,目前掌握的人工智能技術(shù)處于該階段,是沒有理解和推理的感知智能;AGI是能理解、推理和解決問題的機器智能,有知覺和自我意識,屬于認知智能;ASI是在幾乎所有領(lǐng)域都比最聰明的人類大腦都聰明的機器智能,是人工智能技術(shù)發(fā)展的終極目標(biāo)。

    過去六十多年來,三大基石即算法、算力和數(shù)據(jù),共同驅(qū)動著人工智能技術(shù)快速發(fā)展。本文概述了弱人工智能的發(fā)展歷程,即初始時期、知識驅(qū)動時期和數(shù)據(jù)驅(qū)動時期,重點梳理了算法和算力的前沿基礎(chǔ)研究進展和面臨的挑戰(zhàn),闡明了大數(shù)據(jù)在數(shù)據(jù)驅(qū)動時期對人工智能發(fā)展的巨大推動作用,最后從算法、算力、數(shù)據(jù)集和產(chǎn)業(yè)化應(yīng)用四個方面淺析了人工智能技術(shù)的發(fā)展趨勢。




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作者信息:

李美桃

(國家工業(yè)信息安全發(fā)展研究中心人工智能所,北京100040)

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